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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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Improving Open-vocabulary Video Visual Relation Detection with Decomposed Prompt Learning and Relation Adjustment

Improving Open-vocabulary Video Visual Relation Detection wi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ming Pei Yi Tan Yanbin Hao Hao Zhang Jinmeng Wu Basura Fernando Xun Yang School of Information Science and Technology University of Science and Technology of China Hefei China Centre for Frontier AI Research Agency for Science Technology and Research(A*STAR) Singapore Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Open-vocabulary video visual relation detection (VidVRD) expands the scope of detecting object relations in videos to include unseen categories. It marks considerable advancement in recognizing novel relations solely by training on a base set, thus extending the frontiers of automated video understanding. However, the performance of current methods on novel predicates remains significantly inferior to that on base categories. We attribute this discrepancy to two primary factors: (1) A significant task misalignment between the Visual Relation Detection (VRD) task and the pre-trained models’ visual feature extractors, which are often designed for tasks like video-text retrieval and image-text retrieval, resulting in poor generalization to the novel set. (2) The relatively small size and limited vocabulary of open-vocabulary datasets, which create a substantial gap between base and novel predicates. Consequently, text prompts trained on the base set fail to generalize effectively to the novel set. To address these issues, we propose two improvement measures: (1) We decompose base and novel relations into actional and spatial patterns and introduce an innovative text prompt learning method that leverages the shared patterns between base and novel relations. (2) We develop a relation probability adjustment mechanism that utilizes reliable base relation predictions to adjust the probabilities of relations in novel classes by considering their overlaps in either actional or spatial contents. Experimental results on the benchmark dataset demonstrate significant performance improvements.

关键词： Training Learning systems Visualization Vocabulary Signal processing Benchmark testing Feature extraction Acoustics Reliability Speech processing

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MA-VoxelMorph:Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images

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Journal of Innovative Optical Health Sciences 2025年第1期18卷 135-151页

作者： Qing Huang Lei Ren Tingwei Quan Minglei Yang Hongmei Yuan Kai Cao School of Computer Science&Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan Hubei 430074P.R.China Beijing Wandong Medical Technology Co. Ltd.Beijing 100015P.R.China Changhai Hospital of Shanghai Shanghai 200433 P.R.China

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization ***,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is *** deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration *** alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels *** further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding *** method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local *** proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine *** also proved to be fast in 3D image registration,using about 1.5 s,and faster than most *** and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

关键词： Thoracoabdominal CT image registration large deformation fine structure multi-scale attention mechanism

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Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition 4th

Mirror Contrastive Loss Based Sliding Window Transformer for...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819640003

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model

arXiv

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arXiv 2025年

作者： Chen, Jiali Hei, Xusen Xue, Yuqi Wu, Zihan Xie, Jiayuan Cai, Yi Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China School of Software Engineering South China University of Technology China The Hong Kong Polytechnic University Hong Kong

Chinese literary classics hold significant cultural and educational value, offering deep insights into morality, history, and human nature. These works often include classical Chinese and complex narratives, making them difficult for children to read. To bridge this gap, we introduce a child-friendly literary adaptation (CLA) task to adapt the Chinese literary classic into engaging and accessible text for children. However, recent large language models (LLMs) overlook children’s reading preferences (i.e., vivid character portrayals, concise narrative structures, and appropriate readability), which poses challenges in CLA. In this paper, we propose a method called InstructChild, which augments the LLM with these preferences for adaptation. Specifically, we first obtain the characters’ personalities and narrative structure as additional information for fine-grained instruction tuning. Then, we devise a readability metric as the reward to align the LLM with the children’s reading level. Finally, a lookahead decoding strategy is applied to improve the readability of the generated text during inference. To support the evaluation of CLA task, we construct the Classic4Children dataset, which comprises both the original and child-friendly versions of the Four Great Classical Novels of Chinese literature. Experimental results show that our InstructChild significantly improves automatic and human evaluation performance. Copyright © 2025, The Authors. All rights reserved.

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Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning

arXiv

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arXiv 2025年

作者： Liu, Bangzhen Zheng, Chenxi Xu, Xuemiao Xu, Cheng Zhang, Huaidong He, Shengfeng South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology The State Key Laboratory of Subtropical Building and Urban Science The Ministry of Education Key Laboratory of Big Data and Intelligent Robot The Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Singapore Management University Singapore

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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An Improved Dual Neural Network Method Based on Levy Flight for Multi-robot Cooperative Area Coverage Search in 3D Unknown Environments

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Fangfang Wang, Yongqi Wang, Wenhao Xin, Jianbin Peng, Jinzhu Wang, Yaonan Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471039 China National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

The research on multi-robot collaborative search in unknown 3D environments, based on bio-inspired neural networks, holds significant value and ***, challenges arise in 3D environments, including excessive turning and vertical movement, as well as the potential for collisions between robots. In response, we propose an improved Glasius Bio-Inspired Neural Network(GBNN) that mitigates decision conflicts among robots and considers the impact of turning and vertical movement on robot decision-making. Furthermore, to address the issue of robots getting trapped in local deadlocks during the search process, we present a dual neural network algorithm based on Levy flights. In the method Dual Glasius Bio-Inspired Neural Network based on Levy Flight(LF-DUAL-GBNN) proposed in this paper, robots obtain random target points through Levy flights and are then guided by a dual neural network to navigate to the vicinity of the target points, thus breaking free from local deadlock states. Finally, we conducted simulation experiments to validate the algorithm's effectiveness. © 2016 IEEE.

关键词： Microrobots

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HOSVD-Based Subspace Algorithm for Multidimensional Frequency Estimation Without Pairing Parameters

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Chinese Journal of Electronics 2025年第4期23卷 729-734页

作者： Yuntao Wu Longting Huang Hui Cao Yanbin Zhang Key Laboratory of Intelligent Robot in Hubei Province Wuhan Institute of Technology Wuhan China Department of Electronic Engineering City University of Hong Kong Hong Kong SAR China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

In this paper, a new method for multidimensional frequency estimation of multiple sinusoids that combines the HOSVD (Higher-order singular value decomposition) subspace and projection separation approaches is presented. Frequency parameters in the first dimension are obtained by using the signal subspace of the first dimension which is extracted by the HOSVD decomposition. Subsequently, a set of projection separation matrices is constructed to project the measure tensor and separate the components of the received tensor into single ones. And then, the signal subspace of each dimension of separated measure tensor are estimated by the HOSVD decomposition and the desired multidimensional frequency pairing are automatically obtained. Simulation results are included to demonstrate the advantage of the proposed method over two existing methods in terms of performance as well computational load.

关键词： Tensors AWGN Simulation Education Estimation Frequency estimation Matrix decomposition Singular value decomposition

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Word Length-Aware Text Spotting: Enhancing Dense Text Detection and Recognition for Camera-Captured Document Image

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Hao Zhou, Huabing Zhang, Yanduo Ma, Jiayi Ling, Haibin Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430205 China Wuhan University Electronic Information School Wuhan430072 China Stony Brook University Department of Computer Science Stony BrookNY11794 United States

Text spotting in camera-captured document images faces significant challenges, especially with dense text of variable lengths. Existing approaches falter with the long-tailed distribution of word lengths, leading to decreased performance on words with extreme lengths. To address this issue, we present WordLenSpotter, an end-to-end framework incorporating word length awareness to improve detection and recognition across a wide range of word lengths. Our method utilizes a dilated convolutional fusion module in its image encoder and a transformer framework for joint detection and recognition guided by word length priors. Our innovations include a spatial length predictor (SLP) and a length-aware segmentation (LenSeg) proposal head, enhancing the model’s sensitivity to the spatial distribution of text. Evaluated on our newly constructed DSTD1500 dataset and existing public datasets with dense text, WordLenSpotter demonstrates superior text spotting capabilities, especially in handling the diversity of word lengths in dense text scenes. © 1963-2012 IEEE.

关键词： Distribution transformers

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Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation

arXiv

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arXiv 2025年

作者： Gao, Jianqi Pang, Xizheng Liu, Qi Li, Yanjie The Department of Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Inteligence Science and Engineering Harbin Institute of Technology Shenzhen518055 China The Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China

Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas. The high-level policy creates a sub-goal to direct the navigation process. Notably, we have developed a sub-goal update mechanism that considers environment congestion, efficiently avoiding the entrapment of the robot in local minimum areas. The low-level motion planning policy, trained through safe reinforcement learning, outputs real-time control instructions based on acquired sub-goal. Specifically, to enhance the robot’s environmental perception, we introduce a new obstacle encoding method that evaluates the impact of obstacles on the robot’s motion planning. To validate the performance of our HRL-based navigation framework, we conduct simulations in office, home, and restaurant environments. The findings demonstrate that our HRL-based navigation framework excels in both static and dynamic scenarios. Finally, we implement the HRL-based navigation framework on a TurtleBot3 robot for physical validation experiments, which exhibits its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： robot programming

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